Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- mozilla-foundation/common_voice_17_0
+base_model:
+- facebook/w2v-bert-2.0
+---
+
+# Fine-Tune Wav2Vec Bert 2.0 for Jyutping Recogition
+
+## Inference
+
+Please clone the [repo](https://github.com/hon9kon9ize/wav2vec2bert-jyutping) and follow the instructions to run the inference.
+
+```python
+from model import Wav2Vec2BertForCantonese
+from transformers import Wav2Vec2BertProcessor, SeamlessM4TFeatureExtractor, Wav2Vec2CTCTokenizer
+import librosa
+
+model_id = "hon9kon9ize/wav2vec2bert-jyutping"
+
+tokenizer = Wav2Vec2CTCTokenizer(
+    "vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|"
+)
+tone_tokenizer = Wav2Vec2CTCTokenizer(
+    "tone_vocab.json",
+    unk_token="[UNK]",
+    pad_token="[PAD]",
+    word_delimiter_token="|",
+)
+
+# load processor
+feature_extractor = SeamlessM4TFeatureExtractor.from_pretrained(model_id)
+processor = Wav2Vec2BertProcessor(
+    feature_extractor=feature_extractor, tokenizer=tokenizer
+)
+
+model = Wav2Vec2BertForCantonese.from_pretrained(
+    model_id,
+    attention_dropout=0.2,
+    hidden_dropout=0.2,
+    feat_proj_dropout=0.0,
+    mask_time_prob=0.0,
+    layerdrop=0.0,
+    add_adapter=True,
+    ctc_loss_reduction="mean",
+    pad_token_id=processor.tokenizer.pad_token_id,
+    vocab_size=len(processor.tokenizer),
+).eval().cuda()
+
+test_audio = "test.wav"
+
+audio_input, _ = librosa.load(test_audio, sr=16_000)
+input_features = processor(audio_input, return_tensors="pt", sampling_rate=16_000).input_features[0]
+
+output = model.inference(input_features=input_features.unsqueeze(0).cuda(), processor=processor, tone_tokenizer=tone_tokenizer)
+
+print(output) # maa4 maa1 go3 jiu4 jiu2 jiu4 jiu4 juk6 zeoi3
+```